The advent and rapid expansion of communication networks in which mobile communication devices are installed, such as wireless cellular telephone networks, have placed ever-increasing demands on the network operator to maintain and improve network quality. To this end, service providers and other network operators have typically deployed a dedicated Mobile Test Unit (MTU) measurement tool in the network as needed to perform field measurements in response to consumer complaints and other indicators of poor network quality. The MTU is a dedicated hardware test solution deployed on a limited basis to collect measurement data and the number of units deployed is very small in relation to the subscriber population, yielding a statistically small measurement base.
The problems associated with the collection of network field measurements in this way are many. Since the MTU or equivalent is a statistically small sampling of the actual population of users at any given time, the collected data may be statistically suspect on a day-to-day operational basis. The collection of measurement data does not necessarily reflect the occurrence of problems as experienced by an actual user of the network since the deployment of the test units are so sparse relative to the device population. In other words, the MTU may not actual “see” the problem as it is seen by an actual user on the network. Moreover, the deployment of MTUs in an indoor environment is performed in a very limited, diagnostic way, thereby yielding statistically insignificant sampling in that environment. Statistical switch information has been used to try to provide broader statistical sampling that more closely approximates the number of subscriber units. However, the amount of detail provided is often compromised when the network is experiencing significant network problems. Also, collection of data at the switch does not necessarily reflect the actual experience of individual network users.
There is therefore an unmet need in the art to be able to quickly, accurately, and dynamically respond to perceived quality problems occurring within communication networks, in both indoor and outdoor environments, with broad statistical sampling in order to be able to enhance quality of network services perceived by individual network users.